Genome wide association studies (GWAS) pinpoint the specific location in the human genome that is associated with a particular disease. Often, the regions of the genome identified in these studies are non- coding, i.e. do not change the sequence of the protein encoded by a gene. One possible mechanism by which a mutation can cause a disease without changing the protein sequence is by changing the structure of the messenger RNA (mRNA). Changes in the structure of the mRNA (in particular in the untranslated regions (UTRs)) can alter gene regulation. This is because significant components of the cell's regulatory machinery are encoded in the UTRs. This is the case for mutations in the untranslated region of the PTEN gene, which cause a rare genetic disorder called Cowden's Syndrome (which results in Hamartoma and Cancer) (Teresi et al., 2007). We hypothesize that those mutations in UTR mRNA that significantly alter its structure can modify the regulatory machinery of the gene and lead to the disease. We have developed both computational and experimental approaches to assess the structural changes caused by specific mutations in mRNA UTRs. We will scan all known disease-associated mutations in the Human Genetic Mutation database (HGMD) computationally to identify the subset that cause the largest changes in UTR mRNA structure. We will then confirm our findings using a high-throughput experimental approach we have developed that leverages multiplexed capillary electrophoresis technology (Mitra et al., 2008). This will allow us to identify and validate a subset of known disease-associated mutations where the structure of the mRNA is altered by mutation. These data will focus the research aimed at developing molecular treatments for the diseases associated with these mutations on the RNA. Our results will also begin to elucidate molecular mechanisms associated with Human disease in non-coding regions of the Human genome. We aim to identify disease-associated mutations in the human genome that alter the structure of the messenger mRNA using computational and experimental approaches. By identifying the disease-associated mutations that alter regulatory RNAs, we will identify novel molecular targets for therapeutic development.